Performance Evaluation of Machine Learning Applications Using WebAssembly Across Different Programming Languages

WebAssembly (WASM) has emerged as a promising compilation target for languages traditionally not executed on the web and enable the cross-platform deployment of high-performance applications. While its general use cases have been well studied, the performance implications of executing Machine Learning (ML) workloads via WASM across different programming languages and runtime environments remain relatively unexplored. This paper presents a systematic evaluation of two representative ML models, K-Means and Logistic Regression, implemented in Python, Rust, and C++ and compiled to WASM. These models are executed in two distinct environments: a web browser and the WebAssembly System Interface (WASI), and their execution time and accuracy is compared against each programming language across both environments. This study aims to provide insight into the trade-offs and practical considerations involved in deploying ML workloads using WebAssembly across different language ecosystems and runtime configurations.